Domain Adaptive Classification for Compensating Variability in Histopathological Whole Slide Images

  • Michael Gadermayr
  • Martin Strauch
  • Barbara Mara Klinkhammer
  • Sonja Djudjaj
  • Peter Boor
  • Dorit Merhof
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)

Abstract

Histopathological whole slide images of the same organ stained with the same dye exhibit substantial inter-slide variation due to the manual preparation and staining process as well as due to inter-individual variability. In order to improve the generalization ability of a classification model on data from kidney pathology, we investigate a domain adaptation approach where a classifier trained on data from the source domain is presented a small number of user-labeled samples from the target domain. Domain adaptation resulted in improved classification performance, especially when combined with an interactive labeling procedure.

Keywords

Filtration Paraffin 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Michael Gadermayr
    • 1
  • Martin Strauch
    • 1
  • Barbara Mara Klinkhammer
    • 2
  • Sonja Djudjaj
    • 2
  • Peter Boor
    • 2
  • Dorit Merhof
    • 1
  1. 1.Aachen Center for Biomedical Image Analysis, Visualization and Exploration (ACTIVE), Institute of Imaging and Computer VisionRWTH Aachen UniversityAachenGermany
  2. 2.Institute of Pathology, University Hospital AachenRWTH Aachen UniversityAachenGermany

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